R Wheel Running Analysis

Welcome!

This is a Quarto document built to help you analyze .log files from mouse wheel running experiments.

How to use this notebook:

1. Set your ⚙️ options for analysis
2. Run code chunks 1-3 to set up and create your folder structure.
3. Dump all the .log files in the folder created by the script in data > 01_Raw_data.
4. Then run the remaining code chunks in order.
5. To run everything at once and produce a nice HTML output, Click the Render button above

When you execute code within the notebook, the results appear beneath the code. You can run code chunks one by one by clicking the Run button in the upper right corner of the chunk or by placing your cursor the code chunk and pressing Ctrl+Shift+Enter. To run all code chunks, you can Ctrl+Alt+R or hit the Run button and select “Run All” or “Restart R and Run all.”

📚 Setup Libraries

This code block loads necessary libraries and sets up the directory structure necessary for the project. You need to run it, but it is not included in your output notebook.

Loading required package: colorspace

Warning: package 'colorspace' was built under R version 4.1.2

Loading required package: tidyr

Warning: package 'tidyr' was built under R version 4.1.2

Loading required package: here

here() starts at /Users/rsenft/Documents/GitHub/Mouse-Wheel-Running-Analysis

Loading required package: grid

Loading required package: stringr

Loading required package: matrixStats

Loading required package: reshape2

Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

Loading required package: dplyr

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Attaching package: 'dplyr'

The following object is masked from 'package:matrixStats':

    count

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Loading required package: vroom

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Loading required package: fs

Loading required package: progress

Loading required package: data.table

Attaching package: 'data.table'

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Loading required package: ggplot2

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Loading required package: behavr

Loading required package: ggetho

Loading required package: report

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Loading required package: sleepr

Loading required package: zeitgebr

 ~ DONE! ~  1. 25 packages loaded

⚙️ Options for analysis – USER INPUT REQUIRED

• Select your options for your analysis in the code below. TRUE for True, FALSE for False.

Settings for Exp1 : 
      Experiment name : Exp1 
           Lights On  : 7 
           Lights Off : 19 
         Time change?️ : FALSE 

  ~ DONE! ~  02. Settings loaded! 

📁 Make directory structure for the project

 ~ DONE! ~ 3. Made folder structure here:  /Users/rsenft/Documents/GitHub/Mouse-Wheel-Running-Analysis

🧹 Organize, Load, and Clean Data

This step organizes the mouse .log files into 1 folder per mouse, then loads the data into R and creates a metadata table from the data frames.

If you’d like to remove animals, now is the time to remove those folders from data > 02_Organized_data.

🔢 Add columns to data

This chunk takes your data for each mouse and adds additional variables, like speed, time of day, and experimental phase information. Data columns include:

• expDay (1, 2, 3…# of experimental days)
• speed (should be in meters/min)
• elapsedDays same as expDay but days can be fractional
• hour (clock time hour)
• minutes (clock time minutes)
• activeFactor (whether the animal is in its active phase (lights OFF -> TRUE))
• t (time in seconds since the first lights ON (can precede the start of the recording, usually this is lights on on Day 1)
• expPhase (which experimental phase each time point belongs to)
• moving (whether that individual animal moved during that 10 second period))

Now we stitch everything together into 1 big data table. We additionally add an id variable for each experimental mouse that is the mouse ID + the experiment name.

behavr table with: 33 individuals 7 metavariables 10 variables 2.911903e+06 measurements 1 key (id)

~ DONE! ~ 7. 0 mice were removed because they ran less than 1% of the experiment:

📆 Experimental timeline

Now we add in the different phases of the experiment in a expPhase column.

~ DONE! ~ 8. Phase information added to data table. 📋

💾 Save tables

This is optional, if you want to save out tables of binned data for each mouse.

# Would you like to save data every X min? (default True)
saveBinned <- TRUE
desired_bin <- 5 # min

#######################
#Save Metadata table
write.csv(full_metadata, file=here(dir_results, "exp_metdata.csv"))


#Save daily and light cycle data for every mouse
  dt_daily_byLC <- dt %>% dplyr::group_by(id, expDay, activeFactor) %>% dplyr::summarize(avg_speed = mean(speed))

`summarise()` has grouped output by 'id', 'expDay'. You can override using the
`.groups` argument.

    filename <- paste0(expName,"_daily_bylightcycle.csv")
    write.csv(dt_daily_byLC, file=here(dir_results, filename), row.names = FALSE)

#Save just daily data for every mouse
  dt_daily <- dt %>% dplyr::group_by(id, expDay) %>% dplyr::summarize(avg_speed = mean(speed))

`summarise()` has grouped output by 'id'. You can override using the `.groups`
argument.

    filename <- paste0(expName,"_daily.csv")
    write.csv(dt_daily, file=here(dir_results, filename), row.names = FALSE)
  cat(cmark, paste0('9. Finished summarizing daily data for ',nMice,' mice '), '🐁')

~ DONE! ~ 9. Finished summarizing daily data for 33 mice  🐁

if (saveBinned){
  pb <- progress_bar$new(total=nMice)
for (eachid in metadata[,id]){
  df <- full_dt[id==eachid,]
  mouse <- sub("\\|.*", "", eachid)
  t_min <- min(df$t)/behavr::mins(1)
  t_max <- max(df$t)/behavr::mins(1)
  df <- data.frame("MeanSpeed"=tapply(df$speed, cut(df$t/behavr::mins(1), seq(t_min, t_max, by=desired_bin)), mean), "ExpDay"=tapply(df$expDay, cut(df$t/behavr::mins(1), seq(t_min, t_max, by=desired_bin)), median))
  filename <- paste0(mouse,"_",desired_bin,"_min.csv")
  write.csv(df, file=here(dir_table_ind, filename), row.names = F)
  pb$tick()
}

cat('\n', cmark, '9. Finished collapsing data into ',desired_bin,' minute bins (function = mean) for ',nMice,' mice ', '🐁')
}

 ~ DONE! ~ 9. Finished collapsing data into  5  minute bins (function = mean) for  33  mice  🐁

#remove(full_dt)
startDay <- 1
endDay <- max(dt$expDay)

📈 Make graphs

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Actogram graph for all mice

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📑 Methods and References

Analyses were conducted using the R Statistical language (version 4.1.1; R Core Team, 2021) on macOS Big Sur 10.16, using the packages matrixStats (version 0.61.0; Bengtsson H, 2021), progress (version 1.2.2; Csárdi G, FitzJohn R, 2019), data.table (version 1.14.2; Dowle M, Srinivasan A, 2021), sleepr (version 0.3.0; Geissmann Q, 2018), behavr (version 0.3.2; Geissmann Q, 2019), ggetho (version 0.3.6; Geissmann Q, 2020), zeitgebr (version 0.3.5; Geissmann Q, Garcia L, 2020), vroom (version 1.6.0; Hester J et al., 2022), fs (version 1.5.2; Hester J et al., 2021), report (version 0.5.5; Makowski D et al., 2021), here (version 1.0.1; Müller K, 2020), reshape2 (version 1.4.4; Wickham H, 2007), ggplot2 (version 3.3.6; Wickham H, 2016), stringr (version 1.4.0; Wickham H, 2019), dplyr (version 1.0.8; Wickham H et al., 2022), tidyr (version 1.2.0; Wickham H, Girlich M, 2022) and colorspace (version 2.0.3; Zeileis A et al., 2020).

References

• Bengtsson H (2021). matrixStats: Functions that Apply to Rows andColumns of Matrices (and to Vectors). R package version 0.61.0, <URL:https://CRAN.R-project.org/package=matrixStats>.
• Csárdi G, FitzJohn R (2019). progress: Terminal Progress Bars. Rpackage version 1.2.2, <URL:https://CRAN.R-project.org/package=progress>.
• Dowle M, Srinivasan A (2021). data.table: Extension of data.frame.R package version 1.14.2, <URL:https://CRAN.R-project.org/package=data.table>.
• Geissmann Q (2018). sleepr: Analyse Activity and Sleep Behaviour. Rpackage version 0.3.0, <URL:https://CRAN.R-project.org/package=sleepr>.
• Geissmann Q (2019). behavr: Canonical Data Structure for BehaviouralData. R package version 0.3.2, <URL:https://CRAN.R-project.org/package=behavr>.
• Geissmann Q (2020). ggetho: Visualisation of High-ThroughputBehavioural (i.e. Ethomics) Data. R package version 0.3.6, <URL:https://CRAN.R-project.org/package=ggetho>.
• Geissmann Q, Garcia L (2020). zeitgebr: Analysis of CircadianBehaviours. R package version 0.3.5, <URL:https://CRAN.R-project.org/package=zeitgebr>.
• Hester J, Wickham H, Bryan J (2022). vroom: Read and Write RectangularText Data Quickly. R package version 1.6.0, <URL:https://CRAN.R-project.org/package=vroom>.
• Hester J, Wickham H, Csárdi G (2021). fs: Cross-Platform File SystemOperations Based on ‘libuv’. R package version 1.5.2, <URL:https://CRAN.R-project.org/package=fs>.
• Makowski D, Ben-Shachar M, Patil I, Lüdecke D (2021). “AutomatedResults Reporting as a Practical Tool to Improve Reproducibility andMethodological Best Practices Adoption.” CRAN. <URL:https://github.com/easystats/report>.
• Müller K (2020). here: A Simpler Way to Find Your Files. R packageversion 1.0.1, <URL: https://CRAN.R-project.org/package=here>.
• R Core Team (2021). R: A Language and Environment for StatisticalComputing. R Foundation for Statistical Computing, Vienna, Austria.<URL: https://www.R-project.org/>.
• Wickham H (2007). “Reshaping Data with the reshape Package.” Journalof Statistical Software, 21(12), 1-20. <URL:http://www.jstatsoft.org/v21/i12/>.
• Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis.Springer-Verlag New York. ISBN 978-3-319-24277-4, <URL:https://ggplot2.tidyverse.org>.
• Wickham H (2019). stringr: Simple, Consistent Wrappers for CommonString Operations. R package version 1.4.0, <URL:https://CRAN.R-project.org/package=stringr>.
• Wickham H, François R, Henry L, Müller K (2022). dplyr: A Grammar ofData Manipulation. R package version 1.0.8, <URL:https://CRAN.R-project.org/package=dplyr>.
• Wickham H, Girlich M (2022). tidyr: Tidy Messy Data. R packageversion 1.2.0, <URL: https://CRAN.R-project.org/package=tidyr>.
• Zeileis A, Fisher JC, Hornik K, Ihaka R, McWhite CD, Murrell P,Stauffer R, Wilke CO (2020). “colorspace: A Toolbox for Manipulatingand Assessing Colors and Palettes.” Journal of Statistical Software,96(1), 1-49. doi: 10.18637/jss.v096.i01 (URL:https://doi.org/10.18637/jss.v096.i01).Zeileis A, Hornik K, Murrell P (2009). “Escaping RGBland: SelectingColors for Statistical Graphics.” Computational Statistics & DataAnalysis, 53(9), 3259-3270. doi: 10.1016/j.csda.2008.11.033 (URL:https://doi.org/10.1016/j.csda.2008.11.033).Stauffer R, Mayr GJ, Dabernig M, Zeileis A (2009). “Somewhere over theRainbow: How to Make Effective Use of Colors in MeteorologicalVisualizations.” Bulletin of the American Meteorological Society,96(2), 203-216. doi: 10.1175/BAMS-D-13-00155.1 (URL:https://doi.org/10.1175/BAMS-D-13-00155.1).

Session info for configuration

R version 4.1.1 (2021-08-10) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Big Sur 10.16

Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages: [1] grid stats graphics grDevices utils datasets methods
[8] base

other attached packages: [1] zeitgebr_0.3.5 sleepr_0.3.0 report_0.5.5 ggetho_0.3.6
[5] behavr_0.3.2 ggplot2_3.3.6 data.table_1.14.2 progress_1.2.2
[9] fs_1.5.2 vroom_1.6.0 dplyr_1.0.8 reshape2_1.4.4
[13] matrixStats_0.61.0 stringr_1.4.0 here_1.0.1 tidyr_1.2.0
[17] colorspace_2.0-3

loaded via a namespace (and not attached): [1] tidyselect_1.1.2 xfun_0.30 purrr_0.3.4 vctrs_0.4.1
[5] generics_0.1.2 htmltools_0.5.2 yaml_2.3.5 pracma_2.4.2
[9] utf8_1.2.2 rlang_1.0.2 pillar_1.7.0 glue_1.6.2
[13] withr_2.5.0 DBI_1.1.2 RColorBrewer_1.1-3 bit64_4.0.5
[17] lifecycle_1.0.1 plyr_1.8.7 munsell_0.5.0 gtable_0.3.0
[21] htmlwidgets_1.5.4 evaluate_0.15 labeling_0.4.2 knitr_1.38
[25] tzdb_0.3.0 fastmap_1.1.0 datawizard_0.6.3 parallel_4.1.1
[29] fansi_1.0.3 Rcpp_1.0.8.3 scales_1.2.0 jsonlite_1.8.0
[33] farver_2.1.0 bit_4.0.4 hms_1.1.1 digest_0.6.29
[37] stringi_1.7.6 insight_0.18.7 rprojroot_2.0.3 cli_3.2.0
[41] tools_4.1.1 magrittr_2.0.3 tibble_3.1.6 crayon_1.5.1
[45] pkgconfig_2.0.3 ellipsis_0.3.2 prettyunits_1.1.1 assertthat_0.2.1
[49] rmarkdown_2.13 rstudioapi_0.13 R6_2.5.1 compiler_4.1.1